Goal 1

• To learn the basic concepts of programming

We use Python for this:

• we need something ;)
• free, well-documented, cross-platform
• widely-used

Use what your colleagues (tend to) use

Goal 2

• To analyse and visualise experimental data

• Effectiveness of a new treatment for arthritis
• Several patients, recording inflammation on each day

• Tabular (comma-separated) data

We can do this with a little programming

• Why programming? AUTOMATION

Setting up - 1

Before we begin…

• make a neat working environment
• obtain data

cd ~/Desktop
mkdir python-novice-inflammation
cd python-novice-inflammation

LIVE DEMO

Setting up - 2

Before we begin…

• make a neat working environment
• obtain data

cp 2017-03-23-standrews/lessons/python-01/data/python-novice-inflammation-data.zip ./
cp 2017-03-23-standrews/lessons/python-01/data/python-novice-inflammation-code.zip ./
unzip python-novice-inflammation-data.zip
unzip python-novice-inflammation-code.zip

(you can download files via Etherpad)

LIVE DEMO

Starting Jupyter

At the command-line, start Jupyter notebook:

jupyter notebook

Jupyter landing page

Create a new notebook

My first notebook

• Give your notebook a name (variables)

Cell types

• Jupyter documents are comprised of cells
• A Jupyter cell can have one of several types

• Change the first cell to Markdown

Markdown text

• Markdown allows us to enter formatted text.

• Execute a cell with Shift + Enter

Entering code

• Mathematical statements can be entered directly into a code cell

• Execute a cell with Shift + Enter

Exercise (5min)

• Try out some of your own calculations in code cells, e.g.

My first variable

• Let’s create a variable called name, containing "Samia"

Printing a variable

• The print() function shows the contents of a variable

Working with variables

weight_kg = 55
print(weight_kg)
2.2 * weight_kg
print("weight in pounds", 2.2 * weight_kg)
weight_kg = 57.5
print("weight in kilograms is now:", weight_kg)
weight_lb = 2.2 * weight_kg
print('weight in kilograms:', weight_kg, 'and in pounds:', weight_lb)
weight_kg = 100
print('weight in kilograms:', weight_kg, 'and in pounds:', weight_lb)

• LIVE DEMO

Who’s who in memory?

• If you are in a Jupyter notebook or iPython terminal…
• %whos will show you all defined variables

Start a new notebook

• Add a header

Examine the data

• data/inflammation-01.csv

$ head data/inflammation-01.csv 
0,0,1,3,1,2,4,7,8,3,3,3,10,5,7,4,7,7,12,18,6,13,11,11,7,7,4,6,8,8,4,4,5,7,3,4,2,3,0,0
0,1,2,1,2,1,3,2,2,6,10,11,5,9,4,4,7,16,8,6,18,4,12,5,12,7,11,5,11,3,3,5,4,4,5,5,1,1,0,1
0,1,1,3,3,2,6,2,5,9,5,7,4,5,4,15,5,11,9,10,19,14,12,17,7,12,11,7,4,2,10,5,4,2,2,3,2,2,1,1
0,0,2,0,4,2,2,1,6,7,10,7,9,13,8,8,15,10,10,7,17,4,4,7,6,15,6,4,9,11,3,5,6,3,3,4,2,3,2,1
0,1,1,3,3,1,3,5,2,4,4,7,6,5,3,10,8,10,6,17,9,14,9,7,13,9,12,6,7,7,9,6,3,2,2,4,2,0,1,1
0,0,1,2,2,4,2,1,6,4,7,6,6,9,9,15,4,16,18,12,12,5,18,9,5,3,10,3,12,7,8,4,7,3,5,4,4,3,2,1
0,0,2,2,4,2,2,5,5,8,6,5,11,9,4,13,5,12,10,6,9,17,15,8,9,3,13,7,8,2,8,8,4,2,3,5,4,1,1,1
0,0,1,2,3,1,2,3,5,3,7,8,8,5,10,9,15,11,18,19,20,8,5,13,15,10,6,10,6,7,4,9,3,5,2,5,3,2,2,1
0,0,0,3,1,5,6,5,5,8,2,4,11,12,10,11,9,10,17,11,6,16,12,6,8,14,6,13,10,11,4,6,4,7,6,3,2,1,0,0
0,1,1,2,1,3,5,3,5,8,6,8,12,5,13,6,13,8,16,8,18,15,16,14,12,7,3,8,9,11,2,5,4,5,1,4,1,2,0,0

• Plain text, comma-separated
• To load it, use numpy library

Python libraries

• Python contains many powerful, general tools
• Specialised tools are contained in libraries
• We call on libraries, when needed
• Libraries are loaded with import

import numpy
import seaborn

JUPYTER MAGIC

• Another way to get use of some libraries in Jupyter is through magic

%pylab inline
import numpy
import seaborn

• This only works in Jupyter notebooks

numpy, seaborn, pylab

• numpy: work with matrices and arrays in Python
• seaborn: attractive statistical summary graphs

• pylab: numerical operations and visualisation in Python

• Calling %pylab inline shows graphics within the notebook itself

Load data

• numpy provides a function loadtxt() to load tabular data:

numpy.loadtxt(fname='data/inflammation-01.csv', delimiter=',')

• dotted notation tells us loadtxt() belongs to numpy
• fname: an argument expecting the path to a file
• delimiter: an argument expecting the character that separates columns

Loaded data

• The matrix is truncated to fit the screen
• ... indicate missing rows or columns
• If there are no significant digits, they are not shown (1 == 1. == 1.0)
• Assign the matrix to a variable called data

What is our data?

type(data)
print(data.dtype)
print(data.shape)

LIVE DEMO

Members and attributes

• Creating the array created information, too
• Info stored in members or attributes that belong to data
• data.<attribute> e.g. data.shape

Indexing arrays

• Counting of array elements starts at zero, not at one.

print('first value in data:', data[0, 0])
print('middle value in data:', data[30, 20])

LIVE DEMO

Slicing arrays

• Define start and end separated by : (colon).

print(data[0:4, 0:10])
print(data[5:10, 0:10])

LIVE DEMO

More slices, please!

• Don’t specify start, Python assumes the first element
• Don’t specify end, Python assumes the end element

QUESTION: What would : on its own indicate?

small = data[:3, 36:]
print('small is:')
print(small)

LIVE DEMO

Array operations

• arrays know how to perform operations on their values
• +, -, *, /, etc. are elementwise

doubledata = data * 2.0
print('original:')
print(data[:3, 36:])
print('doubledata:')
print(doubledata[:3, 36:])
tripledata = doubledata + data
print('tripledata:')
print(tripledata[:3, 36:])

LIVE DEMO